The use of digital imaging of human anatomy, for medical or other purposes, is ubiquitous and comes in many forms and methods of acquisition. Digital imaging encompasses the conversion (“digitization”) of analog imaging media into digital representations, such as scanning a physical x-ray film, as well as the use of digital detectors on devices such as computed tomography machines, magnetic resonance imagers, and positron emission tomography. In the latter cases, the imaging data exists only digitally. The wide use of computer-based imaging and display systems has been spurred by enhancements in speed, reduction in terms of cost of materials and storage space, as well as ease of transfer, storage, and display, among other factors.
While digital imaging provides computerized representations of a patient's anatomy, the delineation of individual structures (“delineated anatomy” or “contours” of structures such as tissues, organs, and etc.) within those images can be important for diagnostic and/or therapeutic purposes. In clinical practice, this delineation (also called “contouring”) of a patient's anatomy is performed with user-driven, computer-based tools and/or computer-vision-based automated techniques (“auto-contouring”).
Delineated anatomy can serve a number of useful purposes. In radiology, for example, delineated anatomy can be used to aid in the detection of tumors from screening images. In radiation oncology, the delineated anatomy can be used to guide and optimize the planning of cancer treatment, where spatially targeted dosages of ionizing radiation are applied to a tumor or other region containing cancerous or malignant tissue. Growing and rapidly multiplying cancer cells tend to be more susceptible to damage from ionizing radiation as compared with normal cells, and higher dosage administrated by properly planning the applied radiation can preferentially destroy cancerous or malignant tissue. However, ionizing radiation is harmful to both malignant and healthy cells, and so precise spatial targeting of the radiation is important for applying effective radiation therapy to the malignancy while limiting collateral damage to healthy tissue. Typically during a procedure, one or more beams are directed at a tumor. Angular coverage can be achieved by using a plurality of stationary radiation sources distributed around the subject, or by revolving a single radiation source, such as a linear accelerator, around the subject (i.e., tomotherapy). These beams must be controlled such that the targeted tumor receives enough radiation so as to at least partially destroy it, while minimizing the amount of damage to surrounding non-tumorous tissue. In addition, the use of multiple beams from several angles increases the need for accurate contouring, as it is imperative that the shape and intensity be tailored to keep the integrated exposure of certain radiation-sensitive critical organs below a safety threshold.
Radiation therapy is planned in advance for a specific subject based on imaging data acquired for that subject and the resulting structure contours generated on these images. As such, organ-at-risk (OAR) and tumor contouring is a critical step in radiation therapy treatment process.
While both manual and automated approaches to contouring exist, current approaches are prone to errors due to the large degree of inter- and intraobserver variability. These errors can arise from the limitations of medical imaging technology in terms of visualizing human anatomy, which may have insufficient contrast, resolution, or both. Also contributing to errors is the inherent anatomical variability among individuals. Physicians and/or other clinicians often must evaluate and reverify all radiation therapy contours before their use. In current practices, this requires one or more users to conduct a manual evaluation to assess the accuracy of the delineated anatomy and ensure proper surface contouring and labeling. This manual evaluation is both time consuming and relies on user expertise, alertness, and other human factors to identify potential errors in the delineated anatomy. Failure to detect errors in delineated anatomy can lead to complications ranging from negligible to catastrophic within medical procedures that rely on this data. As such, evaluation of the anatomy delineation accuracy is a mandatory and very important step in all cases.
While it is recognized that evaluating a given contouring for quality assurance purposes is an important step in radiation therapy treatment, there remains substantial impediments to reliably automating the process. Previously developed solutions have included atlas-based systems, where population-based “standard” atlas of structures are used as a benchmark. The atlas-based systems typically use Dice coefficients or other similarities indices for validation, generally against some defined landmarks (i.e., a set of points, frequently user-determined). The atlas-based approaches have several deficiencies. First, the variation from person to person in anatomical structures makes using an atlas-based approach difficult. Furthermore, there are a number of different variations in anatomical structures between populations of persons (based, for example, on sex, height, weight, ethnicity, socioeconomic status, medical history, among many other factors). As such, these methods generally result in some broad dataset (one “man” or “woman”, for example), which results in limited overall utility. Also, atlases, once created, tend to remain fixed in terms of what they contain. Thus analyzing an object or structure not contained within the atlas is not an option for an end-user. Finally, an atlas' reliance on landmark-based similarity coefficients may limit the accuracy of the evaluation to locations near the points of interest. The accuracy of employing point-based similarity coefficients is, as a result, largely dependent on the number of points/landmarks used.
Another previously developed approach to evaluating contouring has been the use of patient-specific “gold standard” dataset. This approach also suffers from a number of limitations. Particularly, a gold standard approach requires that some set of delineated anatomy determined for a given person be designated as the “true” set of structures for that person. This can be done, for example, by assuming that the first set of a series of delineated anatomical structures for a patient is the “truth,” such as the first of a time series of images acquired throughout therapy to determine how one or more structures of interest change shape, size, and/or position. Such “gold-standard” algorithms are limited by the fact that the “truth” dataset must itself be validated manually and is thus prone to errors that would then be propagated through all subsequently analyzed images. Point-based similarity indices are also frequently used with these algorithms, carrying with that the limitations as noted above.
There is, therefore, a need in the art for decreasing the time required for evaluating the accuracy of delineated anatomies as well as for increasing the robustness and reliability of these evaluations in identifying any errors in the evaluated delineated anatomy. At the same time, there is a need in the art for an evaluation system that provides those benefits while also allowing for flexibility and customizability to evaluate an array of image modalities, populations of persons, and/or anatomical structures of interest.